Search Results (5)

The presence of seasonal snow cover in the cold season can significantly affect the thermal conditions of the ground. Understanding the change of the snow–soil interface temperature (T_SS) and its environmental impact factors is essential for predicting subnivean species changes and carbon balance in future climatic conditions. An improved Snow Thermal Model (SNTHERM) is employed to quantify T_SS in farmland of Northeast China (NEC) in a 39-year period (1979–2018) firstly. This study also explored the variation tendency of T_SS and its main influencing factors on grid scale. The result shows that annual average T_SS and the difference between T_SS and air temperature (T_DSSA) increased rapidly between 1979 and 2018 in the farmland of NEC, and we used the Mann–Kendall test to further verify the increasing trends of T_SS and T_DSSA on aggregated farmland of NEC. The correlation analysis showed that mean snow depth (M_SD) is the most pivotal control factor in 95% of pixels and T_DSSA increases as M_SD increases. Snow depth can better predict the change of T_SS in deep–snow regions than average winter temperature (T_SA). The results of this study are of great significance for understanding the impact of snow cover on the energy exchange between the ground and the atmosphere in the cold climate. Full article

Validation of the snow process model is an important preliminary work for the snow parameter estimation. The snow grain growth is a continuous and accumulative process, which cannot be evaluated without comparing with the observations in snow season scale. In order to understand the snow properties in the Asian Water Tower region (including Xinjiang province and the Tibetan Plateau) and enhance the use of modeling tools, an extended snow experiment at the foot of the Altay Mountain was designed to validate and improve the coupled physical Snow Thermal Model (SNTHERM) and the Microwave Emission Model of Layered Snowpacks (MEMLS). By matching simultaneously the observed snow depth, geometric grain size, and observed brightness temperature (T_B), with an RMSE of 1.91 cm, 0.47 mm, and 4.43 K (at 36.5 GHz, vertical polarization), respectively, we finalized the important model coefficients, which are the grain growth coefficient and the grain size to exponential correlation length conversion coefficients. When extended to 102 meteorological stations in the 2008–2009 winter, the SNTHERM predicted the daily snow depth with an accuracy of 2–4 cm RMSE, and the coupled SNTHERM-MEMLS model predicted the satellite-observed T_B with an accuracy of 13.34 K RMSE at 36.5 GHz, vertical polarization, with the fractional snow cover considered. Full article

The quantity of liquid water in the snowpack defines its wetness. The temporal evolution of snow wetness’s plays a significant role in wet-snow avalanche prediction, meltwater release, and water availability estimations and assessments within a river basin. However, it remains a difficult task and a demanding issue to measure the snowpack’s liquid water content (LWC) and its temporal evolution with conventional in situ techniques. We propose an approach based on the use of time-domain reflectometry (TDR) and CS650 soil water content reflectometers to measure the snowpack’s LWC and temperature profiles. For this purpose, we created an easily-applicable, low-cost, automated, and continuous LWC profiling instrument using reflectometers at the Cooperative Remote Sensing Science and Technology Center-Snow Analysis and Field Experiment (CREST-SAFE) in Caribou, ME, USA, and tested it during the snow melt period (February–April) immediately after installation in 2014. Snow Thermal Model (SNTHERM) LWC simulations forced with CREST-SAFE meteorological data were used to evaluate the accuracy of the instrument. Results showed overall good agreement, but clearly indicated inaccuracy under wet snow conditions. For this reason, we present two (for dry and wet snow) statistical relationships between snow LWC and dielectric permittivity similar to Topp’s equation for the LWC of mineral soils. These equations were validated using CREST-SAFE in situ data from winter 2015. Results displayed high agreement when compared to LWC estimates obtained using empirical formulas developed in previous studies, and minor improvement over wet snow LWC estimates. Additionally, the equations seemed to be able to capture the snowpack state (i.e., onset of melt, medium, and maximum saturation). Lastly, field test results show advantages, such as: automated, continuous measurements, the temperature profiling of the snowpack, and the possible categorization of its state. However, future work should focus on improving the instrument’s capability to measure the snowpack’s LWC profile by properly calibrating it with in situ LWC measurements. Acceptable validation agreement indicates that the developed snow LWC, temperature, and wetness profiler offers a promising new tool for snow hydrology research. Full article

Snowpack properties like temperature or density are the result of a complex energy and mass balance process in the snowpack that varies temporally and spatially. The Snow Thermal Model (SNTHERM) is a 1-dimensional model, energy and mass balance-driven, that simulates these properties. This article analyzes the simulated snowpack properties using SNTHERM forced with two datasets, namely measured meteorological data at the Cooperative Remote Sensing Science and Technology-Snow Analysis and Field Experiment (CREST-SAFE) site and the National Land Data Assimilation System (NLDAS). The study area is located on the premises of Caribou Municipal Airport at Caribou (ME, USA). The model evaluation is based on properties such as snow depth, snow water equivalent, and snow density, in addition to a layer-by-layer comparison of snowpack properties. The simulations were assessed with precise in situ observations collected at the CREST-SAFE site. The outputs of the SNTHERM model showed very good agreement with observed data in properties like snow depth, snow water equivalent, and average temperature. Conversely, the model was not very efficient when simulating properties like temperature and grain size in different layers of the snowpack. Full article

Accelerated ice flow near the equilibrium line of west-central Greenland Ice Sheet (GIS) has been attributed to an increase in infiltrated surface melt water as a response to climate warming. The assessment of surface melting events must be more than the detection of melt onset or extent. Retrieval of surface melt magnitude is necessary to improve understanding of ice sheet flow and surface melt coupling. In this paper, we report on a new technique to quantify the magnitude of surface melt. Cloud-free dates of June 10, July 5, 7, 9, and 11, 2001 Moderate Resolution Imaging Spectroradiometer (MODIS) daily reflectance Band 5 (1.230-1.250μm) and surface temperature images rescaled to 1km over western Greenland were used in the retrieval algorithm. An optical-thermal feature space partitioned as a function of melt magnitude was derived using a one-dimensional thermal snowmelt model (SNTHERM89). SNTHERM89 was forced by hourly meteorological data from the Greenland Climate Network (GC-Net) at reference sites spanning dry snow, percolation, and wet snow zones in the Jakobshavn drainage basin in western GIS. Melt magnitude or effective melt (E-melt) was derived for satellite composite periods covering May, June, and July displaying low fractions (0-1%) at elevations greater than 2500m and fractions at or greater than 15% at elevations lower than 1000m assessed for only the upper 5 cm of the snow surface. Validation of E-melt involved comparison of intensity to dry and wet zones determined from QSCAT backscatter. Higher intensities (> 8%) were distributed in wet snow zones, while lower intensities were grouped in dry zones at a first order accuracy of ~ ±2%. Full article